Skip to main content
Glama

studmill

by com.studmill
OverviewDiscussionsAdmin

generate_asset

Generate a Roblox-ready 3D asset from a text prompt. Returns a generation_id immediately. Poll get_generation until status is completed (usually 30–90s per variant). Styles: low-poly (default and most likely to fit the 10k target), blocky, stylized-pbr. Finished assets include measured stats and explicit compliance warnings.

Input Schema

NameRequiredDescriptionDefault
styleNoStyle preset. Default low-poly.
promptYesWhat to generate, e.g. 'a medieval treasure chest with gold coins'.
variantsNoNumber of variants to generate (1–4). Default 1. Each variant counts against quota.
size_studsNoOptional real-world size: the longest side of the finished model in studs (1 stud = 0.28 m), e.g. 6 for a park bench, 25 for a small house. Omit to use the model's default size.
tri_budgetNoTriangle budget (500–10000). Default 10000. Models are decimated to it, then measured.

Schema Changelog

Changes observed during successful MCP inspections.

  1. Changed2 schema fields changed
    • addedInput schema / properties / size_studs
      Added value: +{
+  "description": "Optional real-world size: the longest side of the finished model in studs (1 stud = 0.28 m), e.g. 6 for a park bench, 25 for a small house. Omit to use the model's default size.",
+  "maximum": 2048,
+  "minimum": 0.5,
+  "type": "number"
+}
    • changedInput schema / properties / tri_budget / description
      Previous value: -"Triangle budget (500–10000). Default 10000."New value: +"Triangle budget (500–10000). Default 10000. Models are decimated to it, then measured."
  2. First observed

TDQS

A4.1/5.0
Behavior4/5

Does the description disclose side effects, auth requirements, rate limits, or destructive behavior?

With no annotations, the description carries the full behavioral burden and does so well: it discloses the async return contract, the required polling loop, and realistic latency. It also warns that finished assets carry measured stats and compliance warnings, but omits auth requirements or rate limits.

Agents need to know what a tool does to the world before calling it. Descriptions should go beyond structured annotations to explain consequences.

Conciseness5/5

Is the description appropriately sized, front-loaded, and free of redundancy?

Four tight sentences, purpose front-loaded, then return contract, then polling guidance, then capability notes. No sentence is filler and nothing is buried.

Shorter descriptions cost fewer tokens and are easier for agents to parse. Every sentence should earn its place.

Completeness4/5

Given the tool's complexity, does the description cover enough for an agent to succeed on first attempt?

For a five-parameter generation tool with no annotations and no output schema, the description covers the essentials an agent needs: async flow, polling target, latency, and style selection guidance. It stops short of covering quota/cost explicitly (left to the schema) and any auth or failure modes.

Complex tools with many parameters or behaviors need more documentation. Simple tools need less. This dimension scales expectations accordingly.

Parameters4/5

Does the description clarify parameter syntax, constraints, interactions, or defaults beyond what the schema provides?

Schema coverage is already 100%, so the baseline is 3, but the description adds real value beyond it: low-poly is flagged as the default most likely to satisfy the 10k triangle target, and the per-variant timing connects the `variants` parameter to runtime cost. Size and triangle-budget semantics remain schema-only.

Input schemas describe structure but not intent. Descriptions should explain non-obvious parameter relationships and valid value ranges.

Purpose4/5

Does the description clearly state what the tool does and how it differs from similar tools?

States a specific verb and resource ('Generate a Roblox-ready 3D asset from a text prompt') with clear domain framing, so the agent knows exactly what it produces. It does not, however, distinguish itself from the sibling generate_variants, leaving the choice between the `variants` parameter and the separate tool ambiguous.

Agents choose between tools based on descriptions. A clear purpose with a specific verb and resource helps agents select the right tool.

Usage Guidelines4/5

Does the description explain when to use this tool, when not to, or what alternatives exist?

Gives concrete operational guidance: it returns a generation_id immediately and the caller must poll get_generation until status is completed, with a stated 30–90s expectation per variant. It also hints which style to pick by default and why, but says nothing about when to prefer generate_variants over this tool.

Agents often have multiple tools that could apply. Explicit usage guidance like "use X instead of Y when Z" prevents misuse.

All studmill toolsInspect
Try in Browser
One endpoint for every MCP toolRayrun runs every MCP server behind one URL and holds the credentials. Each call is decided per client, tool, and argument, then recorded.ray.runAd

Glama MCP Gateway

Add one secure layer between your agents and this server.

Resources